Abstract
Accuracy of image-guided prostate interventions can be improved by warping (i.e., nonrigid registration) of high-quality multimodal preoperative magnetic resonance images to the intraoperative prostate geometry. Patient-specific biomechanical models have been applied in several studies when predicting the prostate intraoperative deformations for such warping. Obtaining exact patient-specific information about the stress parameter (e.g., Young’s modulus) of the prostate peripheral zone (PZ) and central gland (CG) for such models remains an unsolved problem. In this study, we investigated the effects of ratio of Young’s modulus of the central gland E CG to the peripheral zone E PZ when predicting the prostate intraoperative deformation for ten cases of prostate brachytherapy. The patient-specific prostate models were implemented by means of the specialized nonlinear finite element procedures that utilize total Lagrangian formulation and explicit integration in time domain. The loading was defined by prescribing deformations on the prostate outer surface. The neo-Hookean hyperelastic constitutive model was applied to simulate the PZ and CG mechanical responses. The PZ to CG Young’s modulus ratio E CG:E PZ was varied between 1:1 (upper bound of the literature data) and 1:40 (lower bound of the literature data). The study indicates that the predicted prostate intraoperative deformations and results of the prostate MRIs nonrigid registration obtained using the predicted deformations depend very weakly on the E CG:E PZ ratio.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Afnan J, Tempany C (2010) Update on Prostate Imaging. Urol Clin North Am 37(1):23–25
Stijn W, Heijmink M, Tom W et al (2009) Changes in prostate shape and volume and their implications for radiotherapy after introduction of endorectal balloon as determined by MRI at 3T. Int J Radiation Oncology Biol Phys 73(5):1446–1453
Bharatha A, Hirose M, Hata N et al (2001) Evaluation of three-dimensional finite element-based deformable registration of pre- and intra-operative prostate imaging. Med Phys 28(12): 2551–2560
Haker S, Warfield S, Tempany C (2004) Landmark-guided surface matching and volumetric warping for improved prostate biopsy targeting and guidance. In: MICCAI 2004, LNCS 3216. Springer, Berlin
Crouch J, Pizer S, Chaney S et al (2003) Medially based meshing with finite element analysis of prostate deformation. In: MICCAI 2003, LNCS 2878. Springer, Berlin
Ferrant M, Warfield S, Guttmann C et al (1999) 3D image matching using a finite element based elastic deformation model. In: MICCAI’99: Proceedings of the second international conference on medical image computing and computer-assisted intervention. Springer, London
Hogea C, Abraham F, Biros, et al (2007) A robust framework for soft tissue simulations with application to modeling brain tumor mass-effect in 3D images. Phys Med Biol. 10.1088/0031-9155/52/23/008
Bathe K-J (1996) Finite element procedures. Prentice-Hall, Upper Saddle River
Zhang Y, Goldgof D, Sarkar S et al (2007) A sensitivity analysis method and its application in physics-based nonrigid motion modeling. Ima Vis Comput 25:262–273
Wittek A, Hawkins T, Miller K (2009) On the unimportance of constituitive models in computing brain deformation for image-guided surgery. Biomech Model Mechanobiol 8:77–84
D’Amico A, Cormack R, Tempany C et al (1998) Real-time magnetic resonance image-guided interstitial brachytherapy in the treatment of select patients with clinically localized prostate cancer. Int J Radiation Oncology Biol Phys 42(3):507–515
D Slicer, www.slicer.org
Miller K (2005) Method of testing very soft biological tissues in compression. J Biomech 38:153–158
Joldes GR, Wittek A, Miller K (2009) Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation. Med Image Anal doi:10.1016/ j.media.2008.12.001
Joldes GR, Wittek A, Miller K (2009) Computation of intra-operative brain shift using dynamic relaxation. Comput Methods Appl Mech Engrg 198:3313–3320
Joldes GR, Wittek A, Mathieu C et al (2009) Real-time prediction of brain shift using nonlinear finite element algorithms. In: MICCAI 2009, LNCS 5762. Springer, Berlin
Miller K, Joldes GR, Lance D et al (2007) Total lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation. Commun Numer Meth Engng 23:121–134
Joldes GR, Wittek A, Miller K (2008) Non-locking tetrahedral finite element for surgical simulation. Commun Numer Meth En 25(7):827–836
Zhang M, Nigwekar P, Castaneda B et al (2008) Quantitative characterization of viscoelastic properties of human prostate correlated with histology. Ultrasound in Med and Biol 34(7): 1033–1042
Phipps S, Yang T, Habib F et al (2005) Measurement of tissue mechanical characteristics to distinguish between benign and malignant prostate disease. J Urology 66:447–450
Kemper J, Sinkus R, Lorenzen J et al (2004) MR elastography of the prostate: initial in-vivo application. Fortschr Röntgenstr 176(8): 1094–1099
Krouskop T, Wheeler T, Kallel F et al (1998) Elastic moduli of breast and prostate tissue under compression. Ultrasonic Imaging 20:260–274
Yang T, Leung S, Phipps S et al (2006) In-vitro dynamic micro-probing and the mechanical properties of human prostate tissues. Technol Health Care 14:281–296
Sinkus R, Tanter M, Xydeas T (2005) Viscoelastic shear properties of in vivo breast lesions measured by MR elastography. Magn Reson Imaging (23):159–165
Tempany C, Straus S, Hata N et al (2008) MR-Guided prostate interventions. J Magn Reson Imaging 27:356–367
Dice L (1945) Measures of the amount of ecological association between species. Ecology 26:297–302
Acknowledgments
The financial support of the Australian Research Council (Grants DP0664534, DP1092893, DP0770275, DP1092893, and LX0774754) is gratefully acknowledged. Andriy fedorov, Nobuhiko Hata, and Clare Tempany were supported by NIH grants U41RR019703 and R01CA111288.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this paper
Cite this paper
McAnearney, S. et al. (2011). The Effects of Young’s Modulus on Predicting Prostate Deformation for MRI-Guided Interventions. In: Wittek, A., Nielsen, P., Miller, K. (eds) Computational Biomechanics for Medicine. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9619-0_5
Download citation
DOI: https://doi.org/10.1007/978-1-4419-9619-0_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-9618-3
Online ISBN: 978-1-4419-9619-0
eBook Packages: EngineeringEngineering (R0)